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Using the ROC curve for gauging treatment effect in clinical trials
Author(s) -
Brumback Lyndia C.,
Pepe Margaret S.,
Alonzo Todd A.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2345
Subject(s) - covariate , wilcoxon signed rank test , receiver operating characteristic , mann–whitney u test , nonparametric statistics , statistics , test statistic , statistic , computer science , parametric statistics , clinical trial , econometrics , statistical hypothesis testing , mathematics , medicine
Non‐parametric procedures such as the Wilcoxon rank‐sum test, or equivalently the Mann–Whitney test, are often used to analyse data from clinical trials. These procedures enable testing for treatment effect, but traditionally do not account for covariates. We adapt recently developed methods for receiver operating characteristic (ROC) curve regression analysis to extend the Mann–Whitney test to accommodate covariate adjustment and evaluation of effect modification. Our approach naturally extends use of the Mann–Whitney statistic in a fashion that is analogous to how linear models extend the t ‐test. We illustrate the methodology with data from clinical trials of a therapy for Cystic Fibrosis. Copyright © 2005 John Wiley & Sons, Ltd.